Connectivity

Interaction

Model output

Conditional and Marginal R2
R2 SD CI CI_low CI_high CI_method Component Effectsize
0.5497533 0.0405674 0.95 0.4697120 0.6252302 HDI conditional Bayesian R-squared
0.1485675 0.1553459 0.95 0.0000233 0.4005399 HDI marginal Bayesian R-squared

Posterior draws

Conditional effects

Raw data

Clay

Interaction

Model output

Conditional and Marginal R2
R2 SD CI CI_low CI_high CI_method Component Effectsize
0.5627202 0.0392795 0.95 0.4761295 0.6325768 HDI conditional Bayesian R-squared
0.1678926 0.1764874 0.95 0.0000337 0.4325012 HDI marginal Bayesian R-squared

Posterior draws

Conditional effects

Raw data

Fraction of slopes > 30

Interaction

Model output

Conditional and Marginal R2
R2 SD CI CI_low CI_high CI_method Component Effectsize
0.5533693 0.0430389 0.95 0.4576215 0.6251421 HDI conditional Bayesian R-squared
0.1115205 0.1229691 0.95 0.0000083 0.3349325 HDI marginal Bayesian R-squared

Posterior draws

Conditional effects

Raw data

Fraction of valleys & hollows

Interaction

Model output

Conditional and Marginal R2
R2 SD CI CI_low CI_high CI_method Component Effectsize
0.5557305 0.0412885 0.95 0.4682730 0.6318487 HDI conditional Bayesian R-squared
0.1147872 0.1272852 0.95 0.0000009 0.3509720 HDI marginal Bayesian R-squared

Posterior draws

Conditional effects

Raw data

dNBR

Interaction

Model output

Conditional and Marginal R2
R2 SD CI CI_low CI_high CI_method Component Effectsize
0.5491595 0.0421747 0.95 0.4577560 0.6202150 HDI conditional Bayesian R-squared
0.1908664 0.1805869 0.95 0.0000139 0.4365256 HDI marginal Bayesian R-squared

Posterior draws

Conditional effects

Raw data

Elongation

Interaction

Model output

Conditional and Marginal R2
R2 SD CI CI_low CI_high CI_method Component Effectsize
0.5527765 0.0440311 0.95 0.4599328 0.6260697 HDI conditional Bayesian R-squared
0.0931940 0.1047355 0.95 0.0000360 0.3077291 HDI marginal Bayesian R-squared

Posterior draws

Conditional effects

Raw data

Fraction of recharge areas

Interaction

Model output

Conditional and Marginal R2
R2 SD CI CI_low CI_high CI_method Component Effectsize
0.5575006 0.0421702 0.95 0.4691356 0.6348832 HDI conditional Bayesian R-squared
0.1051653 0.1160475 0.95 0.0000061 0.3619841 HDI marginal Bayesian R-squared

Posterior draws

Conditional effects

Raw data

Fraction of glacial areas

Interaction

Model output

Conditional and Marginal R2
R2 SD CI CI_low CI_high CI_method Component Effectsize
0.5570286 0.0415047 0.95 0.4700102 0.6303055 HDI conditional Bayesian R-squared
0.0963793 0.1117034 0.95 0.0000222 0.3161430 HDI marginal Bayesian R-squared

Posterior draws

Conditional effects

Raw data

Snow persistence

Interaction

Model output

Conditional and Marginal R2
R2 SD CI CI_low CI_high CI_method Component Effectsize
0.5566551 0.0392546 0.95 0.4667264 0.6266153 HDI conditional Bayesian R-squared
0.1009053 0.1136120 0.95 0.0000138 0.3382319 HDI marginal Bayesian R-squared

Posterior draws

Conditional effects

Raw data

Hypso75

Interaction

Model output

Conditional and Marginal R2
R2 SD CI CI_low CI_high CI_method Component Effectsize
0.5563844 0.0415695 0.95 0.4682576 0.6258493 HDI conditional Bayesian R-squared
0.0921855 0.1076866 0.95 0.0000087 0.3367914 HDI marginal Bayesian R-squared

Posterior draws

Conditional effects

Raw data

Ksat

Interaction

Model output

Conditional and Marginal R2
R2 SD CI CI_low CI_high CI_method Component Effectsize
0.5564038 0.0405336 0.95 0.4662464 0.6287134 HDI conditional Bayesian R-squared
0.1107339 0.1191393 0.95 0.0000176 0.3397985 HDI marginal Bayesian R-squared

Posterior draws

Conditional effects

Raw data

Flow length

Interaction

Model output

Conditional and Marginal R2
R2 SD CI CI_low CI_high CI_method Component Effectsize
0.5528571 0.0409670 0.95 0.460947 0.6280460 HDI conditional Bayesian R-squared
0.0816878 0.0958526 0.95 0.000013 0.2893218 HDI marginal Bayesian R-squared

Posterior draws

Conditional effects

Raw data

Fraction of mulch

Interaction

Model output

Conditional and Marginal R2
R2 SD CI CI_low CI_high CI_method Component Effectsize
0.5627626 0.0380185 0.95 0.4777328 0.6263958 HDI conditional Bayesian R-squared
0.2588924 0.2354025 0.95 0.0000670 0.5218313 HDI marginal Bayesian R-squared

Posterior draws

Conditional effects

Raw data

PWD

Interaction

Model output

## 
## Model Info:
##  function:     stan_glmer
##  family:       Gamma [log]
##  formula:      stage_rise_cm ~ MI60_mmhr * lag_pwd + fire + sp_cat + yearCont + 
##     (1 + MI60_mmhr | site_number) + (1 | yearFact)
##  algorithm:    sampling
##  sample:       4500 (posterior sample size)
##  priors:       see help('prior_summary')
##  observations: 247
##  groups:       site_number (26), yearFact (3)
## 
## Estimates:
##                                              mean   sd   10%   50%   90%
## (Intercept)                                 1.2    0.9 -0.1   1.4   2.1 
## MI60_mmhr                                   0.5    0.1  0.4   0.5   0.6 
## lag_pwd                                    -0.1    0.1 -0.3  -0.1   0.1 
## fireetf                                     1.5    0.4  1.0   1.5   1.9 
## sp_catlow                                   0.5    0.4  0.0   0.5   1.0 
## yearCont                                    0.0    0.5 -0.7   0.0   0.6 
## MI60_mmhr:lag_pwd                           0.1    0.1  0.0   0.1   0.3 
## b[(Intercept) site_number:1]               -0.8    0.4 -1.4  -0.8  -0.3 
## b[MI60_mmhr site_number:1]                 -0.3    0.2 -0.6  -0.3  -0.1 
## b[(Intercept) site_number:2]               -1.0    0.3 -1.4  -1.0  -0.5 
## b[MI60_mmhr site_number:2]                 -0.4    0.2 -0.6  -0.4  -0.1 
## b[(Intercept) site_number:3]               -0.5    0.3 -0.9  -0.5  -0.1 
## b[MI60_mmhr site_number:3]                 -0.2    0.2 -0.4  -0.2   0.0 
## b[(Intercept) site_number:4]               -0.5    0.5 -1.2  -0.6   0.1 
## b[MI60_mmhr site_number:4]                  0.0    0.4 -0.4   0.0   0.4 
## b[(Intercept) site_number:5]                0.0    0.3 -0.3   0.0   0.4 
## b[MI60_mmhr site_number:5]                  0.1    0.2 -0.1   0.1   0.4 
## b[(Intercept) site_number:6]                0.9    0.3  0.5   0.9   1.3 
## b[MI60_mmhr site_number:6]                  0.0    0.3 -0.3   0.0   0.3 
## b[(Intercept) site_number:7]                0.2    0.3 -0.3   0.1   0.6 
## b[MI60_mmhr site_number:7]                  0.0    0.3 -0.5   0.0   0.3 
## b[(Intercept) site_number:8]                0.1    0.5 -0.5   0.1   0.8 
## b[MI60_mmhr site_number:8]                 -0.1    0.4 -0.6  -0.1   0.3 
## b[(Intercept) site_number:9]                1.3    0.5  0.7   1.3   1.9 
## b[MI60_mmhr site_number:9]                  0.0    0.4 -0.6   0.0   0.4 
## b[(Intercept) site_number:10]               0.4    0.5 -0.2   0.4   1.0 
## b[MI60_mmhr site_number:10]                 0.1    0.3 -0.3   0.1   0.5 
## b[(Intercept) site_number:11]               0.7    0.4  0.2   0.6   1.1 
## b[MI60_mmhr site_number:11]                 0.5    0.3  0.1   0.5   1.0 
## b[(Intercept) site_number:12]               0.0    0.4 -0.5   0.0   0.5 
## b[MI60_mmhr site_number:12]                -0.1    0.3 -0.5  -0.1   0.2 
## b[(Intercept) site_number:13]               0.6    0.4  0.1   0.6   1.0 
## b[MI60_mmhr site_number:13]                 0.0    0.2 -0.3   0.0   0.3 
## b[(Intercept) site_number:14]               1.1    0.4  0.6   1.1   1.6 
## b[MI60_mmhr site_number:14]                 0.0    0.4 -0.5   0.0   0.4 
## b[(Intercept) site_number:15]               0.2    0.4 -0.3   0.2   0.6 
## b[MI60_mmhr site_number:15]                -0.2    0.2 -0.5  -0.2   0.1 
## b[(Intercept) site_number:16]               0.5    0.3  0.1   0.5   0.9 
## b[MI60_mmhr site_number:16]                 0.1    0.3 -0.2   0.1   0.5 
## b[(Intercept) site_number:17]               0.3    0.4 -0.2   0.3   0.7 
## b[MI60_mmhr site_number:17]                -0.2    0.3 -0.5  -0.1   0.1 
## b[(Intercept) site_number:18]              -1.0    0.4 -1.6  -1.0  -0.5 
## b[MI60_mmhr site_number:18]                -0.1    0.4 -0.5  -0.1   0.3 
## b[(Intercept) site_number:19]              -0.4    0.3 -0.9  -0.4   0.0 
## b[MI60_mmhr site_number:19]                 0.2    0.3 -0.1   0.2   0.6 
## b[(Intercept) site_number:20]               0.9    0.3  0.4   0.8   1.3 
## b[MI60_mmhr site_number:20]                 0.4    0.3  0.1   0.3   0.7 
## b[(Intercept) site_number:21]              -1.3    0.4 -1.8  -1.3  -0.8 
## b[MI60_mmhr site_number:21]                 0.0    0.4 -0.4   0.0   0.5 
## b[(Intercept) site_number:22]              -0.2    0.3 -0.6  -0.2   0.2 
## b[MI60_mmhr site_number:22]                 0.1    0.2 -0.2   0.1   0.4 
## b[(Intercept) site_number:23]               0.5    0.4  0.0   0.5   0.9 
## b[MI60_mmhr site_number:23]                 0.4    0.3  0.0   0.3   0.7 
## b[(Intercept) site_number:24]               0.1    0.4 -0.4   0.1   0.6 
## b[MI60_mmhr site_number:24]                 0.2    0.2 -0.1   0.1   0.5 
## b[(Intercept) site_number:25]               0.6    0.4  0.1   0.6   1.1 
## b[MI60_mmhr site_number:25]                 0.0    0.3 -0.3   0.0   0.3 
## b[(Intercept) site_number:26]              -1.0    0.4 -1.5  -1.0  -0.6 
## b[MI60_mmhr site_number:26]                -0.1    0.2 -0.4  -0.1   0.1 
## b[(Intercept) yearFact:2021]                0.5    1.0 -0.3   0.2   2.0 
## b[(Intercept) yearFact:2022]                0.8    0.9  0.0   0.5   2.1 
## b[(Intercept) yearFact:2023]                0.6    1.0 -0.2   0.3   2.1 
## shape                                       2.2    0.2  1.9   2.2   2.4 
## Sigma[site_number:(Intercept),(Intercept)]  0.7    0.3  0.4   0.6   1.0 
## Sigma[site_number:MI60_mmhr,(Intercept)]    0.1    0.1  0.0   0.1   0.2 
## Sigma[site_number:MI60_mmhr,MI60_mmhr]      0.1    0.1  0.0   0.1   0.3 
## Sigma[yearFact:(Intercept),(Intercept)]     2.5    5.5  0.0   0.5   6.9 
## 
## Fit Diagnostics:
##            mean   sd   10%   50%   90%
## mean_PPD 24.1    2.3 21.2  23.9  27.1 
## 
## The mean_ppd is the sample average posterior predictive distribution of the outcome variable (for details see help('summary.stanreg')).
## 
## MCMC diagnostics
##                                            mcse Rhat n_eff
## (Intercept)                                0.0  1.0  1324 
## MI60_mmhr                                  0.0  1.0  2723 
## lag_pwd                                    0.0  1.0  4699 
## fireetf                                    0.0  1.0  1706 
## sp_catlow                                  0.0  1.0  1807 
## yearCont                                   0.0  1.0  3313 
## MI60_mmhr:lag_pwd                          0.0  1.0  2262 
## b[(Intercept) site_number:1]               0.0  1.0  3117 
## b[MI60_mmhr site_number:1]                 0.0  1.0  3346 
## b[(Intercept) site_number:2]               0.0  1.0  2901 
## b[MI60_mmhr site_number:2]                 0.0  1.0  3283 
## b[(Intercept) site_number:3]               0.0  1.0  1947 
## b[MI60_mmhr site_number:3]                 0.0  1.0  3288 
## b[(Intercept) site_number:4]               0.0  1.0  5186 
## b[MI60_mmhr site_number:4]                 0.0  1.0  6284 
## b[(Intercept) site_number:5]               0.0  1.0  2398 
## b[MI60_mmhr site_number:5]                 0.0  1.0  3404 
## b[(Intercept) site_number:6]               0.0  1.0  2659 
## b[MI60_mmhr site_number:6]                 0.0  1.0  5372 
## b[(Intercept) site_number:7]               0.0  1.0  3192 
## b[MI60_mmhr site_number:7]                 0.0  1.0  6123 
## b[(Intercept) site_number:8]               0.0  1.0  2627 
## b[MI60_mmhr site_number:8]                 0.0  1.0  4163 
## b[(Intercept) site_number:9]               0.0  1.0  2944 
## b[MI60_mmhr site_number:9]                 0.0  1.0  3202 
## b[(Intercept) site_number:10]              0.0  1.0  1768 
## b[MI60_mmhr site_number:10]                0.0  1.0  5795 
## b[(Intercept) site_number:11]              0.0  1.0  2728 
## b[MI60_mmhr site_number:11]                0.0  1.0  2144 
## b[(Intercept) site_number:12]              0.0  1.0  2223 
## b[MI60_mmhr site_number:12]                0.0  1.0  4112 
## b[(Intercept) site_number:13]              0.0  1.0  3135 
## b[MI60_mmhr site_number:13]                0.0  1.0  4484 
## b[(Intercept) site_number:14]              0.0  1.0  3205 
## b[MI60_mmhr site_number:14]                0.0  1.0  4359 
## b[(Intercept) site_number:15]              0.0  1.0  2031 
## b[MI60_mmhr site_number:15]                0.0  1.0  2164 
## b[(Intercept) site_number:16]              0.0  1.0  2671 
## b[MI60_mmhr site_number:16]                0.0  1.0  5278 
## b[(Intercept) site_number:17]              0.0  1.0  2106 
## b[MI60_mmhr site_number:17]                0.0  1.0  3334 
## b[(Intercept) site_number:18]              0.0  1.0  3901 
## b[MI60_mmhr site_number:18]                0.0  1.0  5442 
## b[(Intercept) site_number:19]              0.0  1.0  2622 
## b[MI60_mmhr site_number:19]                0.0  1.0  3741 
## b[(Intercept) site_number:20]              0.0  1.0  2817 
## b[MI60_mmhr site_number:20]                0.0  1.0  2828 
## b[(Intercept) site_number:21]              0.0  1.0  3227 
## b[MI60_mmhr site_number:21]                0.0  1.0  3600 
## b[(Intercept) site_number:22]              0.0  1.0  2900 
## b[MI60_mmhr site_number:22]                0.0  1.0  5902 
## b[(Intercept) site_number:23]              0.0  1.0  2553 
## b[MI60_mmhr site_number:23]                0.0  1.0  2735 
## b[(Intercept) site_number:24]              0.0  1.0  3432 
## b[MI60_mmhr site_number:24]                0.0  1.0  3478 
## b[(Intercept) site_number:25]              0.0  1.0  3523 
## b[MI60_mmhr site_number:25]                0.0  1.0  5163 
## b[(Intercept) site_number:26]              0.0  1.0  3058 
## b[MI60_mmhr site_number:26]                0.0  1.0  4979 
## b[(Intercept) yearFact:2021]               0.0  1.0  1592 
## b[(Intercept) yearFact:2022]               0.0  1.0  1229 
## b[(Intercept) yearFact:2023]               0.0  1.0  1520 
## shape                                      0.0  1.0  3950 
## Sigma[site_number:(Intercept),(Intercept)] 0.0  1.0  1596 
## Sigma[site_number:MI60_mmhr,(Intercept)]   0.0  1.0  2205 
## Sigma[site_number:MI60_mmhr,MI60_mmhr]     0.0  1.0  1269 
## Sigma[yearFact:(Intercept),(Intercept)]    0.1  1.0  2084 
## mean_PPD                                   0.0  1.0  4873 
## log-posterior                              0.3  1.0   788 
## 
## For each parameter, mcse is Monte Carlo standard error, n_eff is a crude measure of effective sample size, and Rhat is the potential scale reduction factor on split chains (at convergence Rhat=1).

Conditional and Marginal R2
R2 SD CI CI_low CI_high CI_method Component Effectsize
0.5573937 0.0430702 0.95 0.4675577 0.6320833 HDI conditional Bayesian R-squared
0.0817641 0.0894100 0.95 0.0000151 0.2862768 HDI marginal Bayesian R-squared

Posterior draws

Conditional effects

Raw data